This invention relates to analysis of electrocardiograms and other physiological data.
Currently there is a lack of tools for physicians and biomedical scientists to do research work with a large amount of physiological data acquired by medical devices. For example, tools do not exist for exporting measurements and waveform data from the original files and evaluating the value of new clinical parameters and algorithms such as QT dispersion, T wave alternans, signal averaging, and heart-rate variability. The currently employed manual methods of extracting measurements and data are laborious and time-consuming and they suffer from high intra- and inter-observer variability and poor reproducibility. A semi-automatic system with options for physician""s review and editing would greatly facilitate clinical research work in cardiology and other branches of medicine.
The present invention is directed to an apparatus and a method for taking multiple physiological signals from different sources as input, applying multiple algorithms in its core and generating results which are exported for use in clinical studies and research. The apparatus has a built-in database and a built-in spreadsheet to provide a unified platform for all clinical research in the medical field, including, but not limited to, clinical core laboratory work and high-end clinical research.
The preferred embodiment of the invention is a clinical research workstation capable of handling a wide range of physiological signals including, but not limited to, resting electrocardiogram (ECG), ambulatory ECG, stress ECG, signal-averaged ECG, intra-cardiac electrical and hemodynamic signals, pulse oximetry signals, blood pressure signals, cardiac output signals, electroencephalogram, electro-oculogram, etc. Analysis of each of these physiological signals is supported in one or more separate modules. The research workstation in accordance with the preferred embodiment is also capable of accepting physiological data from a variety of data sources, such as medical devices and systems, including, but not limited to, electrocardiographs, continuous 12-lead ST segment monitors, Holter recorders, stress ECG systems, defibrillators, patient monitors, home health-care devices, medical data storage/management systems, etc.
In accordance with the preferred embodiment, the research workstation has the capability to export any user-selected data in many output formats and different configurations. Output data will include, but is not limited to, patient demographic information, measurements and waveform signals of both processed and raw data stored in the data file. In addition to the data stored in the file, many measurements and waveforms will also be generated by processing the stored data in the research workstation. Users can select any combination of measurements from a built-in spreadsheet by highlighting the ones they need. A batch processing can be used to export the patient demographics, measurements and/or waveform data from the whole directory or a selected database from a built-in Open Database Connectivity (ODBC) database.
With various physiological data as input from multiple data sources, the system will be able to evaluate new parameters using different algorithms. For example, some high-risk cardiac disease indicators such as signal-averaged ECG parameters, QT dispersion, T wave alternans, and heart rate variability, all from the same patient, can be evaluated at the same time. Algorithms which can be optionally built into the research workstation include, but are not limited to, the following: (1) new measurements with and without user-defined re-analysis from physiological data including, but not limited to, resting ECG, ambulatory ECG, stress ECG, intra-cardiac electrical and hemodynamic signals, and ECG, pulse oximetry and blood pressure signals from neonatal, pediatric and adult patient monitors and defibrillators; (2) interpretation and re-analysis of resting ECG; (3) QT dispersion and T wave alternans; (4) multi-lead vector ECG analysis; (5) signal-averaged ECG processing; (6) ECG mapping and modeling; (7) signal filtering and spectral analysis; and (8) heart-rate variability.
In accordance with a further aspect of the preferred embodiment, the research workstation has a built-in ODBC database (Microsoft Access database). The key parameters are stored automatically into the database, and they can be retrieved, sorted and filtered within the system. With this database, reviewing and editing the measurements and interpretation are very convenient. For example, a few simple operations such as, clicking xe2x80x9cgo forwardxe2x80x9d and xe2x80x9cgo backwardxe2x80x9d buttons will lead the researcher through physiological data files one by one.
In addition, the system has a built-in spreadsheet for selecting for export, reviewing and plotting any of the measurements. The spreadsheet is compatible with standard data analysis software, including, but not limited to, Microsoft Excel and SAS (statistical analysis software) packages, and can be directly saved as a file compatible with standard data analysis software. Researchers can perform most analysis and plotting inside the system, and the spreadsheet interacts with the builtin database seamlessly. A trend of a selected group of physiological parameters/measurements can also be plotted.
The clinical research workstation provides standardized coding/scoring of physiological data, including, but not limited to, Minnesota code, and NOVACODE for resting ECG. The research workstation also provides essential functions needed in core laboratories for clinical studies, including, but not limited to, measuring, reviewing and editing of modifiable time markers in physiological waveforms such as waveform onsets, peaks and offsets, re-analysis based on user-modified markers and serial comparison.
The research workstation software disclosed herein can help physicians advance studies in areas such as disease epidemiology, pharmaceutical research and out-come-based analysis. Using this software, physicians can transform a standard computer with a database program such as Microsoft Access and a speadsheet program such as Microsoft Excel into an ECG research workstation that allows them to quickly and easily study large volumes of ECG data. The research workstation software enables physicians to store, access, review and plot ECG data with point-and-click efficiency.